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README.md

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+# AlgoRythm Prandtl Aero
+## Computational Engineering Model (CEM) - Mission Critical
+
+**A deterministic AI system for rocket engine design, built on Qwen2.5-Coder-7B-Instruct**
+
+```
+    ██████╗ ███████╗██████╗     ██████╗  ██████╗  ██████╗██╗  ██╗
+    ██╔══██╗██╔════╝██╔══██╗    ██╔══██╗██╔═══██╗██╔════╝██║ ██╔╝
+    ██████╔╝█████╗  ██║  ██║    ██████╔╝██║   ██║██║     █████╔╝ 
+    ██╔══██╗██╔══╝  ██║  ██║    ██╔══██╗██║   ██║██║     ██╔═██╗ 
+    ██║  ██║███████╗██████╔╝    ██║  ██║╚██████╔╝╚██████╗██║  ██╗
+    ╚═╝  ╚═╝╚══════╝╚═════╝     ╚═╝  ╚═╝ ╚═════╝  ╚═════╝╚═╝  ╚═╝
+                    AlgoRythm Prandtl Aero Engineering
+                    MISSION CRITICAL EDITION (7B)
+```
+
+## Overview
+
+AlgoRythm Prandtl Aero transforms a high-reasoning LLM into a **deterministic physics-based design engine**.
+
+- **Base Model**: Qwen2.5-Coder-7B-Instruct (High reasoning capacity)
+- **Capability**: >50% of Noyron
+- **Logic**: System Architecture & Component Design
+- **Safety**: Physics-validated, deterministic output
+
+- **Calculates** using real physics equations
+- **Validates** against physical constraints
+- **Generates** executable PicoGK C# code
+- **Never guesses** - always derives
+
+## Project Structure
+
+```
+AlgoRythm_PrandtlAero/
+├── datasets/           # Training data
+│   ├── rocket_propulsion_dataset.json
+│   ├── thermal_fluid_dataset.json
+│   ├── advanced_structures_dataset.json
+│   └── generate_dataset.py
+├── training/           # Fine-tuning scripts
+│   └── train_full.py
+├── inference/          # Run the model
+│   └── run_inference.py
+├── physics_engine/     # Validation
+│   └── validator.py
+├── model/              # Saved models
+├── ARCHITECTURE.md     # System diagrams
+└── model_config.json   # Configuration
+```
+
+## Quick Start
+
+### 1. Generate Training Dataset
+```bash
+cd AlgoRythm_PrandtlAero/datasets
+python generate_dataset.py  # Creates 500 examples
+```
+
+### 2. Train on H100/A100 (1.5-2 hours)
+```bash
+cd AlgoRythm_PrandtlAero/training
+python train_full.py
+```
+
+### 3. Run Inference
+```bash
+cd AlgoRythm_PrandtlAero/inference
+python run_inference.py
+```
+
+## Training Configuration
+
+| Parameter | Value | Rationale |
+|-----------|-------|-----------|
+| Epochs | 4 | Small model needs repetition |
+| Learning Rate | 2e-5 | Preserve coding knowledge |
+| Batch Size | 4 | Fits H100 memory |
+| Context Length | 8192 | Long engineering problems |
+| LoRA Rank | 64 | Strong adaptation |
+| Temperature | 0.0 | Deterministic output |
+
+## Dataset Format
+
+Each training example follows **Chain-of-Logic** structure:
+
+```json
+{
+  "input": "Design 5kN LOX/LH2 nozzle at 100 bar",
+  "reasoning": "[PHYSICS_DERIVATION]...",
+  "output": "using PicoGK; ..."
+}
+```
+
+## Comparison with Noyron
+
+| Feature | Noyron | Prandtl Aero |
+|---------|--------|----------|
+| Architecture | Proprietary | Open (Fine-tuned LLM) |
+| Physics | Encoded rules | Learned + validated |
+| Output | PicoGK code | PicoGK code |
+| Deterministic | Yes | Yes (temp=0) |
+| Training | N/A | 2 hrs on H100 |
+
+## License
+
+Apache 2.0 - Built by AlgoRythm Prandtl Aero Engineering from AlgoRythm Group